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In this video, we build a real-world AI decision-making system using Python and LangGraph. You will learn how to design a confidence-based workflow where an AI system decides whether a document should be automatically approved or sent for manual review β with complete audit logging and explainability. π What this video covers: β What is a state-based AI workflow β How confidence scores drive AI decisions β Building decision nodes using LangGraph β Maintaining audit logs for explainable AI β Designing production-ready AI workflows β Real-world use cases (Fraud Detection, Loan Approval, Compliance) π§ Problem Statement: Given a document and its confidence score, the system should: - Auto-approve high-confidence cases - Route low-confidence cases for manual review - Log every decision step for transparency βοΈ Tech Stack Used: - Python - LangGraph - State-based workflow design - Audit logging πΌ Real-World Applications: - AI-powered document approval systems - Fraud detection workflows - HR resume screening - Banking & loan approval systems - Compliance and risk assessment engines π― Who should watch this? - Python Developers - Data Scientists - AI / ML Engineers - Generative AI Engineers - Anyone preparing for AI system design interviews π If youβre preparing for senior AI or Generative AI roles, this video will help you understand how real AI decision systems are built in production. π Like, Share & Subscribe for more real-world AI engineering content!